Biological network inference using redundancy analysis

Authors:
Patrick E. Meyer;Kevin Kontos;Gianluca Bontempi
Affiliations:
ULB Machine Learning Group, Computer Science Department, Université Libre de Bruxelles, Brussels, Belgium;ULB Machine Learning Group, Computer Science Department, Université Libre de Bruxelles, Brussels, Belgium;ULB Machine Learning Group, Computer Science Department, Université Libre de Bruxelles, Brussels, Belgium
Venue:
BIRD'07 Proceedings of the 1st international conference on Bioinformatics research and development
Year:
2007

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Abstract

The paper presents MRNet, an original method for inferring genetic networks from microarray data. This method is based on Maximum Relevance - Minimum Redundancy (MRMR), an effective information-theoretic technique for feature selection. MRNet is compared experimentally to Relevance Networks (RelNet) and ARACNE, two state-of-the-art information-theoretic network inference methods, on several artificial microarray datasets. The results show that MRNet is competitive with the reference information-theoretic methods on all datasets. In particular, when the assessment criterion attributes a higher weight to precision than to recall, MRNet outperforms the state-of-the-art methods.